The GLOBE Team at the Amman Baccalaureate School has been cooperating with friends of environment society, Amman- Jordan for the past five years. This cooperation has resulted in many students learning much about the use of remotely sensed data for land cover mapping as well as the required protocols for collecting ground sample site information. Our team has used advanced image classification techniques to produce a land cover map of North West Amman region, from Landsat Thematic Mapper imagery. The classification scheme used to make this map was the Modified UNESCO Classification (MUC) scheme. Amman baccalaureate school has conducted numerous daylong ground data collection campaigns, called MUC-A-THONS. These ground-collected land cover sample sites provide an excellent source of data for us.
In our land cover mapping project. The student-collected MUC-A-THON data were used in conjunction with additional ground data collected by our Team to create the map.
We are now making further studies comparing the image of 1997 and that of 2002 as a proceeding step to this project to monitor the environmental changes.
Introduction:
Globally, there is a lack of accurate land cover maps available for use in environmental management that includes monitoring deforestation, biodiversity, water quality, ecosystem health, and urban sprawl. Important decisions are based on these maps.
Students participating in the Global Learning and Observations to Benefit the Environment (GLOBE) Program have the opportunity to provide large quantities of accurate reference data necessary to validate and assess the accuracy of land cover maps representing many areas of the world. (1)
An important question that arose early in the development of the program was, if students from countries around the world could collect environmental data, would that data be useful to scientists? In order to provide reliable data that would be trusted by the scientific community, GLOBE incorporated measurements and protocols developed by scientist/educator teams led by a Scientist Principal Investigator and Educator Co-Principal Investigator (Finarelli, 1998). These protocols offer a consistent method of data collection, and if properly followed, will produce accurate data which can be entered into the GLOBE database and used by the scientific community worldwide (Rock and Lawless, 1997).
Hence, the objectives of this study were:
To generate a land cover map from remotely sensed data,?
The objective of our next studies is to:
To test the overall accuracy of student-collected data ?
To use the student-collected reference data to validate the land cover map?
Literature Review
In this project, the land cover is classified using The Modified UNESCO Classification (MUC) system (UNESCO, 1973). The new system incorporated new land cover types absent from many other classification systems. MUC contains all the characteristics of a good classification system, and allows for every possible land cover type on Earth to be put into a unique land cover class. Each MUC class is a distinct type of land cover, with a name and identification number, or MUC class "code". By using MUC, all the GLOBE data may be compiled into a single regional or global land cover data set. Therefore, ground collected data may be gathered and used to validate remotely sensed data following the same scientific protocols worldwide. There are ten Level 1 classes (Table 1). Classes 0-7 are natural land cover and classes 8-9 are developed. These ten classes can be further broken down into four levels of classification with each level increasing in detail (GLOBE, 1997).
Level 1 Category
0 Closed Forest
1 Woodland
2 Shrubland
3 Dwarf-shrubland
4 Herbaceous Vegetation
5 Barren Land
6 Wetland
7 Open Water
8 Cultivated Land
9 Urban
Friends of the Environment
Amman - Jordan